QuantClaw: Precision Where It Matters for OpenClaw
Autonomous agent systems such as OpenClaw introduce significant efficiency challenges due to long-context inputs and multi-turn reasoning. This results in prohibitively high computational and monetary costs in real-world development. While quantization is a standard approach for reducing cost and latency, its impact on agent performance in realistic scenarios remains unclear. In this work, we analyze quantization sensitivity across diverse complex workflows over OpenClaw, and show that precision requirements are highly task-dependent. Based on this observation, we propose QuantClaw, a plug-and-play precision routing plugin that dynamically assigns precision according to task characteristi...
| Authors | Manyi Zhang, Ji-Fu Li, Zhongao Sun et al. |
|---|---|
| Primary category | cs.AI |
| Published | 24 April 2026 |
| Last updated at source | 24 April 2026 |
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